AI-Based Machine Vision

We use Machine Learning and Cognitive Vision to provide pattern recognition and crucial prediction models to help our clients derive meaningful insights from their video footage. We have also trained ML models to assist with emotion detection on human faces. The accuracy of our solutions has allowed us to implement a variety of AI-based Machine Vision solutions to businesses across various industries.

A few use cases of our solutions are People Counting, Age & Gender Identification, Action Detection, Queue Identification, and Theft Detection. We specialize in real-time user intent detection, analysis, and reporting.

Our solutions allow businesses to easily track and monitor multiple fields of view to detect suspicious activity with real-time inventory theft and fraud detection. We also specialize in AI-based face recognition using database comparisons.

As much as AI has become more efficient and attainable, it is not without cost. Concerns about increased infrastructure and maintenance costs must be addressed as AI is applied to more complex challenges. Performance tuning is one effective way to significantly reduce the cost of a given configuration by efficiently utilizing the existing hardware.

Performance Optimization of AI Retail Analytics Solutions with Intel Hardware and Software Platforms

AI/Machine Learning scientists at Zone24x7 achieved dramatic inference speedup using Intel hardware, software and platform optimization.

1. Intel® Xeon® processors – Zone24x7 were able to achieve levels of performance that previously were only possible with dedicated GPU processor based systems.
2. Intel® Acceleration Stack with Intel® FPGA Programmable Acceleration Card – With the Intel Arria® 10 GX FPGA which leverages the OpenVino® toolkit ML/AI inference is performed efficiently at the edge.
3. Intel® distribution of the OpenVino® toolkit – Zone24x7 scientists applied the Model Optimizer and the Benchmarking Tool (also from Intel) to their models to achieve substantial gains in performance.

Structured data can be integrated across multiple business units, providing meaningful insight into business operations. This data can also be used by Data Science teams for descriptive, prescriptive and predictive analytics which can provide valuable information related to possible business opportunities and threats.